class_docstr: Novograd based on `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks
  <https://arxiv.org/pdf/1905.11286.pdf>`_.
  The code is adapted from the implementations in `Jasper for PyTorch
  <https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechRecognition/Jasper/common/optimizers.py>`_,
  and `OpenSeq2Seq <https://github.com/NVIDIA/OpenSeq2Seq/blob/master/open_seq2seq/optimizers/novograd.py>`_.
  
  Args:
      params: iterable of parameters to optimize or dicts defining parameter groups.
      lr: learning rate. Defaults to 1e-3.
      betas: coefficients used for computing running averages of gradient and its square. Defaults to (0.9, 0.98).
      eps: term added to the denominator to improve numerical stability. Defaults to 1e-8.
      weight_decay: weight decay (L2 penalty). Defaults to 0.
      grad_averaging: gradient averaging. Defaults to ``False``.
      amsgrad: whether to use the AMSGrad variant of this algorithm from the paper
          `On the Convergence of Adam and Beyond <https://arxiv.org/pdf/1904.09237.pdf>`_. Defaults to ``False``.
code: def __setstate__(self, state):
      super().__setstate__(state)
      for group in self.param_groups:
          group.setdefault("amsgrad", False)
  
file_path: MONAI/monai/optimizers/novograd.py
imports: from __future__ import annotations
  from collections.abc import Callable, Iterable
  from typing import TypeVar
  import torch
  from torch.optim import Optimizer
label_desc: Use image node_id 2 for calling the Novograd obj's underlying member method code with example usage: obj.__setstate__(state) without return types